%0 Conference Paper
%F Oral
%T Estimating the similarity of community detection methods based on cluster size distribution
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%A Dao, Vinh-Loc
%A BOTHOREL, Cécile
%A Lenca, Philippe
%< avec comité de lecture
%B Complex Networks 2018, The 7th International Conference on Complex Networks and Their Applications
%C Cambridge, United Kingdom
%8 2018-12-11
%D 2018
%K community detection
%K similarity metric
%K community size
%K comparative analysis
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Discrete Mathematics [cs.DM]
%Z Computer Science [cs]/Data Structures and Algorithms [cs.DS]
%Z Computer Science [cs]/Social and Information Networks [cs.SI]Conference papers
%X Detecting community structure discloses tremendous information about complex networks and unlock promising applied perspectives. Accordingly, a numerous number of community detection methods have been proposed in the last two decades with many rewarding discoveries. Notwithstanding, it is still very challenging to determine a suitable method in order to get more insights into the mesoscopic structure of a network given an expected quality, especially on large scale networks. Many recent efforts have also been devoted to investigating various qualities of community structure associated with detection methods, but the answer to this question is still very far from being straightforward. In this paper, we propose a novel approach to estimate the similarity between community detection methods using the size density distributions of communities that they detect. We verify our solution on a very large corpus of networks consisting in more than a hundred networks of five different categories and deliver pairwise similarities of 16 state-of-the-art and well-known methods. Interestingly, our result shows that there is a very clear distinction between the partitioning strategies of different community detection methods. This distinction plays an important role in assisting network analysts to identify their rule-of-thumb solutions.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01911077/document
%2 https://hal.archives-ouvertes.fr/hal-01911077/file/Cluster_size_distribution_comparisation_algos_detection_community_Dao_Bothorel_Lenca_2018.pdf
%L hal-01911077
%U https://hal.archives-ouvertes.fr/hal-01911077
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ INSTITUT-TELECOM
%~ ENIB
%~ TDS-MACS
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA

Article dans une revue

Simonin Jacques, Puentes John

Automatized integration of a contextual model into a process with data variability

%0 Conference Proceedings
%T Comparison of traffic forecasting methods in urban and suburban context
%+ Data Mining and Machine Learning (DM2L)
%+ Lab-STICC_IMTA_CID_DECIDE
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE)
%+ Geometry Processing and Constrained Optimization (M2DisCo)
%A Salotti, Julien
%A Fenet, Serge
%A Billot, Romain
%A Faouzi, Nour-Eddin, El
%A Solnon, Christine
%< avec comité de lecture
%B Internationale Conference on Tools with Artificial Intelligence (ICTAI)
%C Volos, Greece
%I IEEE
%8 2018-11-05
%D 2018
%K ARIMA
%K VAR
%K k-NN
%K Lasso
%K SVR
%K Variable Selection
%K Traffic Forecasting
%K Time Series
%Z Statistics [stat]/Machine Learning [stat.ML]
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]Conference papers
%X In the context of Connected and Smart Cities, the need to predict short term traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, there is however still no clear view of the requirements involved in network-wide traffic forecasting. In this paper, we study the ability of several state-of-the-art methods to forecast the traffic flow at each road segment. Some of the multivariate methods use the information of all sensors to predict traffic at a specific location, whereas some others rely on the selection of a suitable subset. In addition to classical methods, we also study the advantage of learning this subset by using a new variable selection algorithm based on time series graphical models and information theory. This method has already been successfully used in natural science applications with similar goals, but not in the traffic community. A contribution is to evaluate all these methods on two real-world datasets with different characteristics and to compare the forecasting ability of each method in both contexts. The first dataset describes the traffic flow in the city center of Lyon (France), which exhibits complex patterns due to the network structure and urban traffic dynamics. The second dataset describes inter-urban freeway traffic on the outskirts of the french city of Marseille. Experimental results validate the need for variable selection mechanisms and illustrate the complementarity of forecasting algorithms depending on the type of road and the forecasting horizon.
%G English
%2 https://hal.archives-ouvertes.fr/hal-01895136/document
%2 https://hal.archives-ouvertes.fr/hal-01895136/file/main.pdf
%L hal-01895136
%U https://hal.archives-ouvertes.fr/hal-01895136
%~ CNRS
%~ UNIV-BREST
%~ UNIV-UBS
%~ EC-LYON
%~ INSTITUT-TELECOM
%~ ENIB
%~ LAB-STICC
%~ IFSTTAR
%~ UNIV-LYON1
%~ INSA-LYON
%~ ENTPE
%~ LIRIS
%~ LAB-STICC_IMTA_CID_DECIDE
%~ LYON2
%~ UNIV-LYON2
%~ INSA-GROUPE
%~ IMT-ATLANTIQUE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA

%0 Conference Proceedings
%T Detecting and Hunting Cyberthreats in a Maritime Environment: Specification and Experimentation of a Maritime Cybersecurity Operations Centre
%+ Chaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
%+ Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
%+ Département Systèmes Réseaux, Cybersécurité et Droit du numérique (SRCD)
%+ Lab-STICC_IMTA_CID_IRIS
%+ Lab-STICC_IMTA_CID_DECIDE
%A Jacq, Olivier
%A Boudvin, Xavier
%A Brosset, David
%A Kermarrec, Yvon
%A Simonin, Jacques
%< avec comité de lecture
%Z 19267
%( Proceedings Cyber Security In Networking Conference
%B Cyber Security In Networking Conference
%C Paris, France
%P .
%8 2018-10-24
%D 2018
%K ICS
%K SOC
%K Maritime
%K Cyber situation awareness
%Z Computer Science [cs]/Computers and Society [cs.CY]Conference papers
%X The vast majority of worldwide goods exchanges are made by sea. In some parts of the world, the concurrence for dominance at sea is very high and definitely seen as a main military goal. Meanwhile, new generation ships highly rely on information systems for communication, navigation and platform management. This ever-spreading attack surface and permanent satellite links have grown a concern about the potential impact of cyberattacks on a ship at sea or on naval shore infrastructures. Therefore, on top of the usual cyberprotection measures taken for safety reasons, it is essential to implement an ongoing cyber monitoring of ships in order to detect, react accordingly, and stop any incoming threat. In this paper, we explain the specific constraints when trying to assess the cyber situation awareness of maritime information systems. As we will demonstrate, those systems combine physical and logical constraints which complexify their cyber monitoring process and architecture. Gathering valuable data while having a limited and controlled impact on the satellite bandwidth, maintaining a high level of integrity on remote systems in production are, for instance, thriving challenges for both civilian and military ships. We have designed and set up a research platform which fulfils those specifications to streamline the cyber monitoring process.We will then describe the architecture used to detect cyber-threats and collect potential Indices of Compromise from naval systems, as well as the results we have currently achieved.
%G English
%L hal-01911640
%U https://hal.archives-ouvertes.fr/hal-01911640
%~ CNRS
%~ UNIV-UBS
%~ UNIV-BREST
%~ INSTITUT-TELECOM
%~ ENIB
%~ TDS-MACS
%~ LAB-STICC
%~ IMT-ATLANTIQUE
%~ IMTA_SRCD
%~ LAB-STICC_IMTA_CID_IRIS
%~ LAB-STICC_IMTA_CID_DECIDE
%~ IMTA_LUSSI
%~ LAB-STICC_IMTA